ExamPassdump덤프공부가이드는 업계에서 높은 인지도를 자랑하고 있습니다. ExamPassdump제품은 업데이트가 가장 빠르고 적중율이 가장 높아 업계의 다른 IT공부자료 사이트보다 출중합니다. ExamPassdump의Oracle인증 1z0-1195-26덤프는 이해하기 쉽고 모든Oracle인증 1z0-1195-26시험유형이 모두 포함되어 있어 덤프만 잘 이해하고 공부하시면 시험패스는 문제없습니다.
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Working with AI and Vector Foundations | 15% | - Apply vector distance and indexing concepts to similarity search needs - Describe AI, AGI, and machine learning foundations - Explain vectors, embeddings, and the Oracle VECTOR data type |
| Topic 2: Working with JSON and Graph in Oracle AI Database | 20% | - Describe core graph concepts and graph analytic capabilities - Distinguish when graph capabilities and Property Graph Views fit a business use case - Explain JSON and Oracle AI Database JSON capabilities |
| Topic 3: Identifying Oracle AI Database 26ai Strategy and Autonomous AI Database Basics | 20% | - Describe Autonomous AI Database characteristics, offerings, and deployment choices - Create an Autonomous AI Database Serverless instance for a basic workload - Explain modern data characteristics and the Oracle AI Database 26ai converged strategy |
| Topic 4: Building Low-Code Applications and Agentic AI | 10% | - Choose the appropriate Agent Factory capability for a no-code AI agent use case - Describe Oracle APEX as Oracle's low-code platform |
| Topic 5: Using Oracle Database Actions and Data Studio Tools | 15% | - Apply Data Studio capabilities to data discovery, integration, analysis, and sharing tasks - Describe Database Actions and core development tools |
| Topic 6: Implementing Select AI and AI Vector Search in Autonomous AI Database | 20% | - Determine how AI Vector Search supports GenAI pipelines and RAG - Describe Select AI in Autonomous AI Database - Apply AI Vector Search to combined semantic and business-data search scenarios |
Oracle인증 1z0-1195-26시험은 IT업종종사분들에게 널리 알려진 유명한 자격증을 취득할수 있는 시험과목입니다. Oracle인증 1z0-1195-26시험은 영어로 출제되는만큼 시험난이도가 많이 높습니다.하지만 ExamPassdump의Oracle인증 1z0-1195-26덤프만 있다면 아무리 어려운 시험도 쉬워집니다. 오르지 못할 산도 정복할수 있는게ExamPassdump제품의 우점입니다. ExamPassdump의Oracle인증 1z0-1195-26덤프로 시험을 패스하여 자격증을 취득하면 정상에 오를수 있습니다.
질문 # 12
What happens after a user asks a business question with Select AI?
정답：A
설명：
Select AI automates the interaction among the user's natural-language prompt, database metadata, the configured large language model, generated SQL, and returned results. The uploaded assessment therefore correctly identifies option D. Oracle's Select AI documentation states that Autonomous AI Database processes the natural-language prompt, augments it with relevant metadata, interacts with an LLM, generates SQL, and can execute that SQL to return information.
Schema metadata is particularly important. Oracle can augment the prompt with table names, column names and data types, comments, annotations, constraints, and relationship information. This provides the LLM with database context and improves SQL generation while reducing hallucination risk.
Depending on the Select AI action, the service can display generated SQL, execute it, explain it, narrate query results in natural language, perform RAG against vector stores, or communicate directly with an LLM.
Select AI does not disable SQL; SQL remains fundamental to natural-language-to-SQL processing. Nor must users manually generate embeddings for ordinary NL2SQL requests. Embeddings become relevant to RAG
/vector workflows but are not a prerequisite for basic Select AI SQL generation.
Study Guide reference: Implementing Select AI and AI Vector Search in Autonomous AI Database - prompt augmentation, LLM interaction, NL2SQL, and natural-language answers.
질문 # 13
Which output can Select AI deliver to an application?
정답：D
설명：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Select AI supports multiple response modes depending on the action requested. With the default runsql action, Oracle generates SQL from the natural-language prompt, executes it, and returns the resulting data. The showsql action returns the generated SQL statement without executing it, while narrate executes the generated query and sends its results to the configured LLM to produce a natural-language description. Oracle additionally supports actions such as explainsql, chat, and summarize. Therefore, an application can receive a database result set, generated SQL, or a narrative response depending on how Select AI is invoked. Graph visualizations, patch-history maintenance recommendations, and automatic JSON export files are not Select AI output modes. This directly aligns with Oracle's Select AI actions and natural-language database interaction capabilities.
질문 # 14
What describes the role of the Database Actions menu in Autonomous AI Database?
정답：D
설명：
Database Actions serves as the primary web-based launchpad for built-in Autonomous AI Database tooling.
Oracle describes Database Actions, also known as SQL Developer Web, as an integrated interface containing development, data, administration, monitoring, and download features. From this environment, users can access SQL Worksheet, Data Modeler, REST, JSON tools, Oracle Machine Learning, Graph Studio, Oracle APEX, Data Studio functions, database-user administration, Data Pump, Performance Hub, and other database services.
Its role is therefore broader than any single database technology. Graph Studio is one tool accessible through the environment rather than a replacement for SQL. Network isolation is configured through Autonomous AI Database networking facilities such as private endpoints, not by treating Database Actions itself as a network- access mechanism. Likewise, vector-index settings represent only one narrow area of database functionality and do not define the Database Actions environment. Oracle also documents Data Studio as a feature area accessed through Database Actions, reinforcing the concept of Database Actions as a common launchpad for multiple specialized workspaces. The uploaded question set marks the central built-in-tools location as the correct response.
Study Guide reference: Using Oracle Database Actions and Data Studio Tools - Database Actions Launchpad, development tools, Data Studio, administration, and monitoring.
질문 # 15
A development team wants to receive patches before the regular maintenance schedule so they can validate changes early.
Which maintenance option should they select?
정답：D
설명：
Comprehensive and Detailed 100 to 150 words of Explanation From Oracle AI Database topics:
Oracle Autonomous AI Database supports Regular and Early patch levels or maintenance schedules. The Early option applies patches before the Regular schedule so development and test systems can validate upcoming changes before production systems receive them. Oracle's current documentation states that Early patches are applied one week before the Regular scheduled patch and explicitly recommends Early for development and test databases when organizations want advance validation. Regular follows the normal maintenance cycle. "Late maintenance" and "Application-controlled maintenance" are not the applicable patch-level choices for this Serverless scenario. Because the team specifically wants patches before the regular schedule for early validation, the correct selection is Early maintenance. This falls under Autonomous AI Database operational basics, maintenance, and patch-management concepts. Oracle Docs
질문 # 16
A vector index will not fit entirely in memory. Which index organization option should be considered for use?
정답：D
설명：
NEIGHBOR PARTITIONS is the correct index organization when an entirely memory-resident vector graph is unsuitable. The uploaded assessment identifies NEIGHBOR PARTITIONS as the intended answer.
Oracle AI Vector Search distinguishes two primary approximate vector-index organizations: INMEMORY NEIGHBOR GRAPH , based on HNSW, and NEIGHBOR PARTITIONS , based on IVF.
HNSW is specifically an in-memory graph structure. Oracle documentation describes HNSW indexes as specialized memory-only structures and provides vector-memory-pool facilities for holding them. By contrast, the IVF-based Neighbor Partition index organizes vectors into centroid-based partitions and narrows each approximate search to relevant partitions rather than maintaining the complete graph as an in-memory HNSW structure.
EXACT SEARCH ONLY is not an index organization and would typically require evaluating a broader candidate set, sacrificing the scalability benefits of approximate indexing. TARGET ACCURACY is a parameter governing the accuracy/performance trade-off of approximate searches, not an index organization.
INMEMORY NEIGHBOR GRAPH directly conflicts with the stated memory constraint.
Study Guide reference: Working with AI and Vector Foundations - vector index organizations, IVF
/Neighbor Partitions, HNSW/In-Memory Neighbor Graph, and approximate similarity search.
질문 # 17
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